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作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

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Access Control Policy Based on Friend Circle

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computers, Materials & Continua 2020年第3期62卷 1143-1159页

作者： Qin Liu Tinghuai Ma Fan Xing Yuan Tian Abdullah Al-Dhelaan Mohammed Al-Dhelaan School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China CICAEET Jiangsu Engineering Centre of Network MonitoringNanjing University of Information Science and TechnologyNanjing210044China Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia

Nowadays,the scale of the user’s personal social network(personal network,a network of the user and their friends,where the user we call“center user”)is becoming larger and more *** is difficult to find a suitable way to manage them *** order to solve this problem,we propose an access control model for social network to protect the privacy of the central users,which achieves the access control accurately and *** on the hybrid friend circle detection algorithm,we consider the aspects of direct judgment,indirect trust judgment and malicious users,a set of multi-angle control method which could be adapted to the social network environment is ***,we propose the solution to the possible conflict of rights in the right control,and assign the rights reasonably in the case of guaranteeing the privacy of the users.

关键词： Social network privacy protection circle of friends access control

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A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

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computers, Materials & Continua 2020年第9期64卷 1435-1452页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow100031Poland

In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical ***,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical *** ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest *** the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same ***,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT *** performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the *** results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image *** maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.

关键词： Digital watermarking logistic hash algorithm ROI IWT

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Cubic convolutional neural network for hyperspectral image classification 6th

Cubic convolutional neural network for hyperspectral image c...

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Sun, Le Song, Xiangbo Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789811581007

In recent years, the framework of convolutional neural network based on deep learning (DL) has made good progress in the field of hyperspectral image (HSI) classification. In order to improve the classification accuracy and reduce the time complexity of the model, we proposed an end-to-end cubic convolution neural network (C-CNN) framework for the classification of HSI. C-CNN uses one-dimensional convolution and principal component analysis (PCA) operation respectively to reduce the dimension of the original HSI, and merges the processed data. Then through convolving the data cube from the spatial domain and the spatial-spectral domain respectively, deep features of HSI are obtained. Batch normalization and dropout layers are used to prevent overfitting. The proposed C-CNN framework can achieve the optimal results within 80 epochs. Experimental results with widely used hyperspectral image datasets show that the proposed method surpasses the existing state-of-the-art methods based on DL. © Springer Nature Singapore Pte Ltd 2020.

关键词： Convolution

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A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

SSRN

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SSRN 2023年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity. © 2023, The Authors. All rights reserved.

关键词： Simulation platform

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Impact Force Magnitude and Location Recognition of Composite Materials

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computers, Materials & Continua 2020年第9期64卷 1647-1656页

作者： Yajie Sun Yanqing Yuan Qi Wang Sai Ji Lihua Wang Shao’en Wu Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncieUSA

In order to identify the location and magnitude of the impact force accurately,determine the damage range of the structure and accelerate the health monitoring of key components of the composite,this paper studies the location and magnitude of the impact force of composite plates by an inverse ***,a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force,which is from several impact locations,and establish transfer functions between the impact location and the PZT ***,this paper applies several forces to any location on the material plate,and collects the corresponding response signals,and reconstructs the impact force of several locations in ***,according to the reconstruction result of each location,the correct impact location is ***,an improved regularization method is used to optimize the reconstructed impact force and accurate the magnitude of the impact *** comparison experiments prove that the recognition error of this method is smaller.

关键词： Impact force identification transfer function regularization

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Quantum Searchable Encryption for Cloud Data Based on Delegating Quantum Computing 9th

Quantum Searchable Encryption for Cloud Data Based on Delega...

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Xu, Yinsong Liu, Wenjie Chen, Junxiu Tong, Lian School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Information Engineering Jiangsu Maritime Institute Nanjing211100 China

ISBN: (纸本)9783030485122

Based on delegating quantum computing (DQC), a DQC model that adapts to multi-qubit and composite quantum circuits is given firstly. In this model, the single client with limited quantum ability can give her encrypted data to a powerful but untrusted quantum data server and let the data server computes over the encrypted data without decryption, where the computation is a quantum circuit composed of multiple quantum gates. Then, the client generates the decryption key to decrypt the computing result according to the circuit of computation. However, this model cannot meet the situation of multi-client accessing or computing encrypted cloud data in the cloud environment. To solve this problem, we let the client outsource key generation to a trusted key server, which composes the quantum cloud center with the data server. The clients only perform X and Z operation according to the encryption or decryption key. Then, combined with Grover algorithm, a quantum searchable encryption scheme for cloud data based on delegating quantum computing is proposed in this paper. The data server mainly uses Grover algorithm to perform search computation on the encrypted data. Moreover, a concrete example of our scheme is discussed next, where the data server searches for 2 target items from 8 items of the encrypted data. Finally, security of our proposed scheme is analysed, which can protect the security of the data. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Trusted computing

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An Improvement of Coherent Semantic Attention for Image Inpainting 1

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Weiqing Gu, Enming Fang, Wei School of Computer & Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu Key Laboratory of Computer Information Process Technology SooChow University Suzhou China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

In some image restoration algorithms of the past, they often do not consider the continuity between pixels, and the internal features of the hole region. The mapping to the image semantically does not take into account the continuity of the feature, resulting in the color of the fault. Or the deformation of the edge contour of the image. In some of the current popular algorithms and models, we can clearly see the color faults and artificial repair traces from their repair results. These discontinuities are mainly because these methods ignore the semantic relevance and feature continuity of the hole region. Therefore, if we want to get a better image repair effect. We have to improve on semantic relevance and feature continuity. We validated the effectiveness of our proposed method in image restoration tasks on the CelebA and Places2 datasets, and our results yielded a better visual experience in some images than existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection

Semi-supervised stacked autoencoder-based deep hierarchical ...

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作者： Yuan, Chengsheng Chen, Xianyi Yu, Peipeng Meng, Ruohan Cheng, Weijin Wu, Q. M. Jonathan Sun, Xingming School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authentication systems is that most fingerprint scanners are vulnerable to presentation attacks by artificial replicas, made from plastic clay, gelatin, silicon, wood glue, etc. One anti-spoofing attack scheme, called real-time fingerprint liveness detection (RFLD), has been proposed to discriminate live or fake fingerprints. Currently, to resolve the presentation attacks, most RFLD solutions all relied on handcrafted feature extraction and selection. The features extracted by manual method are shallow features of the samples;however, autoencoder can automatically learn deep hierarchical semantic features representation of the samples, thus replacing the operations extracted with hand-designed features. In this paper, we apply stacked autoencoder to RFLD to significantly lower the work-force burden of the feature extraction engineering, and our model consists of two parts: parameter pre-training based on unsupervised learning and FLD based on supervised learning. The performance has been verified on two public fingerprint datasets: LivDet 2011 and 2013, and the experimental results indicate that our proposed approach works well for RFLD as well as the detection performance is satisfactory. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

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Journal on Internet of Things 2021年第2期3卷 65-76页

作者： Zejun Yang Denghui Xia Jin Liu Chao Zheng Yanzhen Qu Yadang Chen Chengjun Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Yukun(Beijing)Network Technology Co. Ltd.Beijing102200China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology Colorado Technical UniversityColorado Springs80907USA

Collaborative filtering algorithms(CF)and mass diffusion(MD)algorithms have been successfully applied to recommender systems for years and can solve the problem of information ***,both algorithms suffer from data sparsity,and both tend to recommend popular products,which have poor diversity and are not suitable for real *** this paper,we propose a user internal similarity-based recommendation algorithm(UISRC).UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal *** internal similarity of users is combined to modify the recommendation score to make score predictions and *** experiments on RYM and *** datasets,the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

关键词： Collaborative filtering mass diffusion recommendation accuracy recommendation system user internal similarity

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